This series of files compile all analyses done during Chapter 2:

All analyses have been done with R 3.6.2.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

To assess Section 1, click here.
To go back to the summary page, click here.

Sources of activity considered for the analyses:

Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):

Gear Code Years Events Species
Trap FishTrap 6 (2010-15) 1061 Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus
Bottom-trawl FishTraw 2 (2013-14) 2 Pandalus borealis
Net FishNet 1 (2010) 5 Clupea harengus, Gadus morhua
Dredge FishDred 5 (2010-14) 21 Mactromeris polynyma

1. Explorations

1.1. Relationships between parameters

This section explores relationships between each pair of parameters or AH distances.

Fist, we can compute the Spearman’s correlation between each parameter.

Correlation coefficients between habitat parameters and metals concentrations
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N H J city dredging_collect dredging_dump industry mooring sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge cumulative_influence
om 1 -0.379 -0.708 0.68 0.023 0.57 0.486 0.606 0.593 0.586 0.568 0.537 0.587 0.605 -0.026 -0.121 0.122 0.136 -0.034 0.43 0.308 0.218 0.518 0.24 0.401 -0.073 0.365 -0.501 -0.195 -0.012 -0.157 0.33
gravel -0.379 1 0.186 -0.471 0.099 -0.127 -0.23 -0.194 -0.23 -0.232 -0.199 -0.207 -0.169 -0.235 0.029 0.007 0.012 0.07 -0.068 -0.16 -0.08 -0.174 -0.217 0.001 -0.073 -0.06 -0.168 0.171 0.322 0.031 0.019 -0.145
sand -0.708 0.186 1 -0.788 -0.308 -0.648 -0.504 -0.587 -0.483 -0.407 -0.524 -0.611 -0.588 -0.529 0.059 0.084 0.031 -0.027 0.28 -0.17 -0.102 -0.002 -0.42 -0.31 -0.469 0.324 -0.17 0.48 0.025 -0.117 0.165 -0.13
silt 0.68 -0.471 -0.788 1 0.05 0.548 0.482 0.506 0.441 0.362 0.449 0.566 0.519 0.493 -0.054 -0.013 -0.068 -0.055 -0.152 0.178 0.079 0.045 0.423 0.247 0.391 -0.196 0.175 -0.463 -0.22 0.035 -0.144 0.148
clay 0.023 0.099 -0.308 0.05 1 0.208 0.095 0.129 0.11 0.062 0.144 0.052 0.15 0.107 -0.098 -0.076 -0.044 0.02 -0.02 0.016 0.02 0.025 0.004 0.241 0.238 -0.032 0.037 -0.092 -0.095 0.063 -0.001 0.088
arsenic 0.57 -0.127 -0.648 0.548 0.208 1 0.797 0.862 0.791 0.656 0.78 0.707 0.902 0.87 -0.266 -0.149 -0.193 0.008 -0.069 0.216 0.076 0.062 0.659 0.522 0.71 -0.158 0.212 -0.585 -0.134 0.107 -0.391 0.279
cadmium 0.486 -0.23 -0.504 0.482 0.095 0.797 1 0.818 0.694 0.564 0.717 0.75 0.886 0.853 -0.308 -0.042 -0.291 -0.133 -0.016 0.185 -0.085 0 0.728 0.467 0.701 -0.14 0.141 -0.45 -0.25 -0.047 -0.321 0.256
chromium 0.606 -0.194 -0.587 0.506 0.129 0.862 0.818 1 0.909 0.818 0.917 0.753 0.92 0.939 -0.331 -0.167 -0.273 -0.041 -0.022 0.436 0.102 0.265 0.733 0.596 0.764 -0.103 0.409 -0.396 -0.18 0.048 -0.459 0.48
copper 0.593 -0.23 -0.483 0.441 0.11 0.791 0.694 0.909 1 0.808 0.842 0.715 0.872 0.952 -0.298 -0.172 -0.225 -0.025 0.236 0.57 0.218 0.439 0.751 0.641 0.755 0.169 0.536 -0.369 -0.204 0.018 -0.54 0.646
iron 0.586 -0.232 -0.407 0.362 0.062 0.656 0.564 0.818 0.808 1 0.878 0.461 0.664 0.785 -0.377 -0.273 -0.251 0.034 0.112 0.651 0.324 0.511 0.67 0.579 0.675 0.061 0.644 -0.342 -0.151 0.044 -0.548 0.665
manganese 0.568 -0.199 -0.524 0.449 0.144 0.78 0.717 0.917 0.842 0.878 1 0.658 0.798 0.846 -0.287 -0.096 -0.261 -0.085 -0.022 0.557 0.208 0.425 0.769 0.671 0.823 -0.082 0.561 -0.356 -0.191 0.124 -0.552 0.589
mercury 0.537 -0.207 -0.611 0.566 0.052 0.707 0.75 0.753 0.715 0.461 0.658 1 0.845 0.755 -0.234 -0.084 -0.199 -0.075 -0.066 0.237 -0.044 0.054 0.731 0.509 0.703 -0.142 0.194 -0.454 -0.191 -0.001 -0.324 0.283
lead 0.587 -0.169 -0.588 0.519 0.15 0.902 0.886 0.92 0.872 0.664 0.798 0.845 1 0.939 -0.304 -0.135 -0.252 -0.051 0.027 0.316 0.049 0.132 0.738 0.599 0.769 -0.065 0.277 -0.488 -0.194 0.032 -0.356 0.395
zinc 0.605 -0.235 -0.529 0.493 0.107 0.87 0.853 0.939 0.952 0.785 0.846 0.755 0.939 1 -0.32 -0.145 -0.253 -0.056 0.15 0.471 0.151 0.308 0.79 0.603 0.764 0.067 0.429 -0.454 -0.218 0.011 -0.485 0.541
S -0.026 0.029 0.059 -0.054 -0.098 -0.266 -0.308 -0.331 -0.298 -0.377 -0.287 -0.234 -0.304 -0.32 1 0.561 0.705 -0.052 -0.14 -0.158 0.077 -0.115 -0.253 -0.313 -0.331 -0.069 -0.151 0.158 0.133 -0.043 0.261 -0.249
N -0.121 0.007 0.084 -0.013 -0.076 -0.149 -0.042 -0.167 -0.172 -0.273 -0.096 -0.084 -0.135 -0.145 0.561 1 -0.041 -0.683 -0.062 -0.162 -0.17 -0.136 -0.021 -0.115 -0.067 -0.061 -0.169 0.181 0.039 -0.043 0.064 -0.152
H 0.122 0.012 0.031 -0.068 -0.044 -0.193 -0.291 -0.273 -0.225 -0.251 -0.261 -0.199 -0.252 -0.253 0.705 -0.041 1 0.598 -0.086 -0.015 0.285 0.023 -0.243 -0.305 -0.329 -0.003 -0.016 0.031 0.032 -0.022 0.307 -0.169
J 0.136 0.07 -0.027 -0.055 0.02 0.008 -0.133 -0.041 -0.025 0.034 -0.085 -0.075 -0.051 -0.056 -0.052 -0.683 0.598 1 0.011 0.053 0.236 0.07 -0.129 -0.106 -0.147 0.035 0.058 -0.136 -0.03 0.079 0.124 -0.032
city -0.034 -0.068 0.28 -0.152 -0.02 -0.069 -0.016 -0.022 0.236 0.112 -0.022 -0.066 0.027 0.15 -0.14 -0.062 -0.086 0.011 1 0.367 0.05 0.291 0.296 0.322 0.159 0.969 0.255 -0.069 -0.245 -0.038 -0.404 0.577
dredging_collect 0.43 -0.16 -0.17 0.178 0.016 0.216 0.185 0.436 0.57 0.651 0.557 0.237 0.316 0.471 -0.158 -0.162 -0.015 0.053 0.367 1 0.722 0.896 0.587 0.545 0.52 0.435 0.956 -0.126 -0.276 -0.092 -0.48 0.91
dredging_dump 0.308 -0.08 -0.102 0.079 0.02 0.076 -0.085 0.102 0.218 0.324 0.208 -0.044 0.049 0.151 0.077 -0.17 0.285 0.236 0.05 0.722 1 0.733 0.181 0.169 0.144 0.17 0.764 -0.135 -0.093 -0.148 -0.085 0.508
industry 0.218 -0.174 -0.002 0.045 0.025 0.062 0 0.265 0.439 0.511 0.425 0.054 0.132 0.308 -0.115 -0.136 0.023 0.07 0.291 0.896 0.733 1 0.359 0.488 0.419 0.37 0.947 0.063 -0.307 -0.046 -0.391 0.814
mooring 0.518 -0.217 -0.42 0.423 0.004 0.659 0.728 0.733 0.751 0.67 0.769 0.731 0.738 0.79 -0.253 -0.021 -0.243 -0.129 0.296 0.587 0.181 0.359 1 0.591 0.766 0.239 0.498 -0.518 -0.308 -0.046 -0.635 0.637
sewers_rain 0.24 0.001 -0.31 0.247 0.241 0.522 0.467 0.596 0.641 0.579 0.671 0.509 0.599 0.603 -0.313 -0.115 -0.305 -0.106 0.322 0.545 0.169 0.488 0.591 1 0.891 0.279 0.572 -0.18 -0.228 0.247 -0.654 0.731
sewers_waste 0.401 -0.073 -0.469 0.391 0.238 0.71 0.701 0.764 0.755 0.675 0.823 0.703 0.769 0.764 -0.331 -0.067 -0.329 -0.147 0.159 0.52 0.144 0.419 0.766 0.891 1 0.068 0.522 -0.36 -0.264 0.049 -0.663 0.632
wharves_city -0.073 -0.06 0.324 -0.196 -0.032 -0.158 -0.14 -0.103 0.169 0.061 -0.082 -0.142 -0.065 0.067 -0.069 -0.061 -0.003 0.035 0.969 0.435 0.17 0.37 0.239 0.279 0.068 1 0.331 -0.006 -0.212 0.004 -0.346 0.608
wharves_industry 0.365 -0.168 -0.17 0.175 0.037 0.212 0.141 0.409 0.536 0.644 0.561 0.194 0.277 0.429 -0.151 -0.169 -0.016 0.058 0.255 0.956 0.764 0.947 0.498 0.572 0.522 0.331 1 -0.064 -0.27 -0.027 -0.463 0.871
fisheries_trap -0.501 0.171 0.48 -0.463 -0.092 -0.585 -0.45 -0.396 -0.369 -0.342 -0.356 -0.454 -0.488 -0.454 0.158 0.181 0.031 -0.136 -0.069 -0.126 -0.135 0.063 -0.518 -0.18 -0.36 -0.006 -0.064 1 0.198 0.07 0.21 -0.117
fisheries_trawl -0.195 0.322 0.025 -0.22 -0.095 -0.134 -0.25 -0.18 -0.204 -0.151 -0.191 -0.191 -0.194 -0.218 0.133 0.039 0.032 -0.03 -0.245 -0.276 -0.093 -0.307 -0.308 -0.228 -0.264 -0.212 -0.27 0.198 1 0.086 0.034 -0.301
fisheries_net -0.012 0.031 -0.117 0.035 0.063 0.107 -0.047 0.048 0.018 0.044 0.124 -0.001 0.032 0.011 -0.043 -0.043 -0.022 0.079 -0.038 -0.092 -0.148 -0.046 -0.046 0.247 0.049 0.004 -0.027 0.07 0.086 1 -0.118 0.026
fisheries_dredge -0.157 0.019 0.165 -0.144 -0.001 -0.391 -0.321 -0.459 -0.54 -0.548 -0.552 -0.324 -0.356 -0.485 0.261 0.064 0.307 0.124 -0.404 -0.48 -0.085 -0.391 -0.635 -0.654 -0.663 -0.346 -0.463 0.21 0.034 -0.118 1 -0.607
cumulative_influence 0.33 -0.145 -0.13 0.148 0.088 0.279 0.256 0.48 0.646 0.665 0.589 0.283 0.395 0.541 -0.249 -0.152 -0.169 -0.032 0.577 0.91 0.508 0.814 0.637 0.731 0.632 0.608 0.871 -0.117 -0.301 0.026 -0.607 1

For the regressions, several types of models were considered: linear, quadratic, exponential and logarithmic. Only linear and quadratic models were implemented as there are some bugs with the calculation of the others. The model with the highest \(R^{2}\) is presented on each plot.

OM

Gravel

Sand

Silt

Clay

Arsenic

Cadmium

Chromium

Copper

Iron

Manganese

Mercury

Lead

Zinc

S

N

H

J

CityInf

InduInf

DredColl

DredDump

MoorSite

RainSew

WastSew

CityWha

InduWha

FishTrap

FishTraw

FishNet

FishDred

Cumulative Influence

1.2. Species abundances by cumulative influence group

Phylum abundances by group
  Phylum Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Group 7 Group 8 Group 9 Group 10
Annelida Annelida NA NA 18 31 22.3 33.3 47.5 NA NA NA
Arthropoda Arthropoda NA NA 9 22.3 41.4 56.5 27.5 NA NA NA
Cnidaria Cnidaria NA NA 0.5 0 0 0 0 NA NA NA
Echinodermata Echinodermata NA NA 53 1.61 3.19 1.78 0.5 NA NA NA
Mollusca Mollusca NA NA 14.5 15.6 15.6 11.2 32 NA NA NA
Nematoda Nematoda NA NA 2.5 26.1 10.6 2.96 0 NA NA NA
Nemertea Nemertea NA NA 0 0.522 0 0.0727 0 NA NA NA
Sipuncula Sipuncula NA NA 0 0.13 0.192 0.345 1 NA NA NA

2. Hierarchical Modelling of Species Communities

We will use the probabilities and indices of influences calculated in Section 1 here. The aim is to obtain predictive models for the benthic communities, based on the abiotic parameters and the human activities.

HMSC models have been developped in a dedicated script, and the R workspace has been imported here.

First, we initiate the HMSC model with the chosen data, priors and parameters.

Here are the diagnostics to evaluate each model’s quality.

Human activities

Trace plots

Explanatory power

Confidence intervals

Variance partitioning

Habitat parameters

Trace plots

Explanatory power

Confidence intervals

Variance partitioning

All variables

Trace plots

Explanatory power

Confidence intervals

Variance partitioning

Finally, we can predict the values of our parameters within BSI.